

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__snake_case				:      List[str]   		=			random.Random()


def 					_UpperCAmelCase				(						a__  ,	a__=1.0  ,	a__=None  ,	a__=None):

			'''simple docstring'''



			if rng is None:
						a_						:						Optional[int] 							= global_rng

			a_						:						List[Any] 							= []
			for batch_idx in range(shape[0]):
						values.append([])
						for _ in range(shape[1]):
									values[-1].append(rng.random() * scale)

			return values



@require_torch
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=7  ,      _lowercase=400  ,      _lowercase=2_000  ,      _lowercase=1  ,      _lowercase=0.0  ,      _lowercase=16_000  ,      _lowercase=True  ,      _lowercase=80  ,      _lowercase=16  ,      _lowercase=64  ,      _lowercase="hann_window"  ,      _lowercase=80  ,      _lowercase=7_600  ,      _lowercase=1e-10  ,      _lowercase=True  ,      )     -> Dict:
									a_						:						int 							= parent
									a_						:						str 							= batch_size
									a_						:						str 							= min_seq_length
									a_						:						Optional[int] 							= max_seq_length
									a_						:						Dict 							= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_						:						int 							= feature_size
									a_						:						Any 							= padding_value
									a_						:						str 							= sampling_rate
									a_						:						Optional[int] 							= do_normalize
									a_						:						Optional[int] 							= num_mel_bins
									a_						:						List[Any] 							= hop_length
									a_						:						Any 							= win_length
									a_						:						int 							= win_function
									a_						:						Union[str, Any] 							= fmin
									a_						:						Optional[int] 							= fmax
									a_						:						Union[str, Any] 							= mel_floor
									a_						:						int 							= return_attention_mask





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "do_normalize": self.do_normalize,
									    "num_mel_bins": self.num_mel_bins,
									    "hop_length": self.hop_length,
									    "win_length": self.win_length,
									    "win_function": self.win_function,
									    "fmin": self.fmin,
									    "fmax": self.fmax,
									    "mel_floor": self.mel_floor,
									    "return_attention_mask": self.return_attention_mask,
									}





						def 			UpperCamelCase__					(    self  ,      _lowercase=False  ,      _lowercase=False    )     -> Tuple:
									def _flatten(_lowercase    ):
												return list(itertools.chain(*_lowercase    )    )

									if equal_length:
												a_						:						List[Any] 							= floats_list((self.batch_size, self.max_seq_length)    )
									else:
												# make sure that inputs increase in size
												a_						:						str 							= [
												    _flatten(floats_list((x, self.feature_size)    )    )
												    for x in range(self.min_seq_length  ,      self.max_seq_length  ,      self.seq_length_diff    )
												]

									if numpify:
												a_						:						Dict 							= [np.asarray(_lowercase    ) for x in speech_inputs]

									return speech_inputs





						def 			UpperCamelCase__					(    self  ,      _lowercase=False  ,      _lowercase=False    )     -> Dict:
									if equal_length:
												a_						:						Any 							= [floats_list((self.max_seq_length, self.num_mel_bins)    ) for _ in range(self.batch_size    )]
									else:
												# make sure that inputs increase in size
												a_						:						int 							= [
												    floats_list((x, self.num_mel_bins)    )
												    for x in range(self.min_seq_length  ,      self.max_seq_length  ,      self.seq_length_diff    )
												]

									if numpify:
												a_						:						Dict 							= [np.asarray(_lowercase    ) for x in speech_inputs]

									return speech_inputs







@require_torch
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[int]       					=			SpeechTaFeatureExtractor





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[str] 							= SpeechTaFeatureExtractionTester(self    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[Any]:
									self.assertTrue(np.all(np.mean(_lowercase  ,      axis=0    ) < 1e-3    )    )
									self.assertTrue(np.all(np.abs(np.var(_lowercase  ,      axis=0    ) - 1    ) < 1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> int:
									# Tests that all call wrap to encode_plus and batch_encode_plus
									a_						:						int 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									# create three inputs of length 800, 1000, and 1200
									a_						:						Optional[Any] 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]
									a_						:						List[str] 							= [np.asarray(_lowercase    ) for speech_input in speech_inputs]

									# Test not batched input
									a_						:						str 							= feat_extract(speech_inputs[0]  ,      return_tensors="""np"""    ).input_values
									a_						:						Union[str, Any] 							= feat_extract(np_speech_inputs[0]  ,      return_tensors="""np"""    ).input_values
									self.assertTrue(np.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )

									# Test batched
									a_						:						int 							= feat_extract(_lowercase  ,      return_tensors="""np"""    ).input_values
									a_						:						Optional[Any] 							= feat_extract(_lowercase  ,      return_tensors="""np"""    ).input_values
									for enc_seq_a, enc_seq_a in zip(_lowercase  ,      _lowercase    ):
												self.assertTrue(np.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						int 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									a_						:						Tuple 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]

									a_						:						List[str] 							= ["""longest""", """max_length""", """do_not_pad"""]
									a_						:						Optional[int] 							= [None, 1_600, None]
									for max_length, padding in zip(_lowercase  ,      _lowercase    ):
												a_						:						Tuple 							= feat_extract(_lowercase  ,      padding=_lowercase  ,      max_length=_lowercase  ,      return_tensors="""np"""    )
												a_						:						List[Any] 							= processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]    )
												self.assertTrue(input_values[0][800:].sum() < 1e-6    )
												self._check_zero_mean_unit_variance(input_values[1][:1_000]    )
												self.assertTrue(input_values[0][1_000:].sum() < 1e-6    )
												self._check_zero_mean_unit_variance(input_values[2][:1_200]    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						List[Any] 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									a_						:						str 							= range(800  ,      1_400  ,      200    )
									a_						:						str 							= [floats_list((1, x)    )[0] for x in lengths]

									a_						:						Any 							= ["""longest""", """max_length""", """do_not_pad"""]
									a_						:						Any 							= [None, 1_600, None]

									for max_length, padding in zip(_lowercase  ,      _lowercase    ):
												a_						:						Optional[int] 							= feat_extract(_lowercase  ,      max_length=_lowercase  ,      padding=_lowercase    )
												a_						:						Optional[int] 							= processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]    )
												self._check_zero_mean_unit_variance(input_values[1][:1_000]    )
												self._check_zero_mean_unit_variance(input_values[2][:1_200]    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						int 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									a_						:						Optional[Any] 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]
									a_						:						Optional[Any] 							= feat_extract(
									    _lowercase  ,      truncation=_lowercase  ,      max_length=1_000  ,      padding="""max_length"""  ,      return_tensors="""np"""    )
									a_						:						List[str] 							= processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1]    )
									self._check_zero_mean_unit_variance(input_values[2]    )





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Dict 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									a_						:						Optional[int] 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]
									a_						:						Union[str, Any] 							= feat_extract(
									    _lowercase  ,      truncation=_lowercase  ,      max_length=1_000  ,      padding="""longest"""  ,      return_tensors="""np"""    )
									a_						:						Optional[int] 							= processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1, :1_000]    )
									self._check_zero_mean_unit_variance(input_values[2]    )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertTrue(input_values.shape == (3, 1_000)    )

									a_						:						Dict 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]
									a_						:						List[str] 							= feat_extract(
									    _lowercase  ,      truncation=_lowercase  ,      max_length=2_000  ,      padding="""longest"""  ,      return_tensors="""np"""    )
									a_						:						Optional[int] 							= processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1, :1_000]    )
									self._check_zero_mean_unit_variance(input_values[2]    )

									# make sure that if max_length > longest -> then pad to longest
									self.assertTrue(input_values.shape == (3, 1_200)    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Tuple 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									a_						:						Tuple 							= np.random.rand(100    ).astype(np.floataa    )
									a_						:						List[Any] 							= np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
												a_						:						str 							= feature_extractor.pad([{"""input_values""": inputs}]  ,      return_tensors="""np"""    )
												self.assertTrue(np_processed.input_values.dtype == np.floataa    )
												a_						:						Optional[Any] 							= feature_extractor.pad([{"""input_values""": inputs}]  ,      return_tensors="""pt"""    )
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									# Tests that all call wrap to encode_plus and batch_encode_plus
									a_						:						Union[str, Any] 							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									# create three inputs of length 800, 1000, and 1200
									a_						:						int 							= [floats_list((1, x)    )[0] for x in range(800  ,      1_400  ,      200    )]
									a_						:						Optional[int] 							= [np.asarray(_lowercase    ) for speech_input in speech_inputs]

									# Test feature size
									a_						:						int 							= feature_extractor(audio_target=_lowercase  ,      padding=_lowercase  ,      return_tensors="""np"""    ).input_values
									self.assertTrue(input_values.ndim == 3    )
									self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins    )

									# Test not batched input
									a_						:						Optional[Any] 							= feature_extractor(speech_inputs[0]  ,      return_tensors="""np"""    ).input_values
									a_						:						Optional[Any] 							= feature_extractor(np_speech_inputs[0]  ,      return_tensors="""np"""    ).input_values
									self.assertTrue(np.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )

									# Test batched
									a_						:						Optional[Any] 							= feature_extractor(_lowercase  ,      return_tensors="""np"""    ).input_values
									a_						:						str 							= feature_extractor(_lowercase  ,      return_tensors="""np"""    ).input_values
									for enc_seq_a, enc_seq_a in zip(_lowercase  ,      _lowercase    ):
												self.assertTrue(np.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )

									# Test 2-D numpy arrays are batched.
									a_						:						Optional[int] 							= [floats_list((1, x)    )[0] for x in (800, 800, 800)]
									a_						:						int 							= np.asarray(_lowercase    )
									a_						:						Union[str, Any] 							= feature_extractor(_lowercase  ,      return_tensors="""np"""    ).input_values
									a_						:						Tuple 							= feature_extractor(_lowercase  ,      return_tensors="""np"""    ).input_values
									for enc_seq_a, enc_seq_a in zip(_lowercase  ,      _lowercase    ):
												self.assertTrue(np.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						List[str] 							= self.feat_extract_tester.prepare_inputs_for_target()
									a_						:						List[str] 							= self.feature_extraction_class(**self.feat_extract_dict    )
									a_						:						Dict 							= feat_extract.model_input_names[0]

									a_						:						int 							= BatchFeature({input_name: speech_inputs}    )

									self.assertTrue(all(len(_lowercase    ) == len(_lowercase    ) for x, y in zip(_lowercase  ,      processed_features[input_name]    )    )    )

									a_						:						Optional[Any] 							= self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowercase    )
									a_						:						List[Any] 							= BatchFeature({input_name: speech_inputs}  ,      tensor_type="""np"""    )

									a_						:						str 							= processed_features[input_name]

									if len(batch_features_input.shape    ) < 3:
												a_						:						Union[str, Any] 							= batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.num_mel_bins)    )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowercase    )
									a_						:						List[str] 							= self.feature_extraction_class(**self.feat_extract_dict    )
									a_						:						int 							= feat_extract.model_input_names[0]

									a_						:						Tuple 							= BatchFeature({input_name: speech_inputs}  ,      tensor_type="""pt"""    )

									a_						:						List[Any] 							= processed_features[input_name]

									if len(batch_features_input.shape    ) < 3:
												a_						:						Dict 							= batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.num_mel_bins)    )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Optional[Any] 							= self.feature_extraction_class(**self.feat_extract_dict    )
									a_						:						int 							= self.feat_extract_tester.prepare_inputs_for_target()
									a_						:						List[str] 							= feat_extract.model_input_names[0]

									a_						:						Union[str, Any] 							= BatchFeature({input_name: speech_inputs}    )

									a_						:						List[str] 							= feat_extract.num_mel_bins  # hack!

									a_						:						Any 							= feat_extract.pad(_lowercase  ,      padding="""longest"""  ,      return_tensors="""np"""    )[input_name]
									a_						:						Tuple 							= feat_extract.pad(_lowercase  ,      padding="""longest"""  ,      return_tensors="""pt"""    )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1e-2    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						List[Any] 							= self.feat_extract_dict
									a_						:						Dict 							= True
									a_						:						int 							= self.feature_extraction_class(**_lowercase    )
									a_						:						List[Any] 							= self.feat_extract_tester.prepare_inputs_for_target()
									a_						:						Optional[Any] 							= [len(_lowercase    ) for x in speech_inputs]
									a_						:						List[str] 							= feat_extract.model_input_names[0]

									a_						:						Any 							= BatchFeature({input_name: speech_inputs}    )

									a_						:						Tuple 							= feat_extract.num_mel_bins  # hack!

									a_						:						Union[str, Any] 							= feat_extract.pad(_lowercase  ,      padding="""longest"""  ,      return_tensors="""np"""    )
									self.assertIn("""attention_mask"""  ,      _lowercase    )
									self.assertListEqual(list(processed.attention_mask.shape    )  ,      list(processed[input_name].shape[:2]    )    )
									self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						List[str] 							= self.feat_extract_dict
									a_						:						Optional[int] 							= True
									a_						:						Union[str, Any] 							= self.feature_extraction_class(**_lowercase    )
									a_						:						Optional[Any] 							= self.feat_extract_tester.prepare_inputs_for_target()
									a_						:						str 							= [len(_lowercase    ) for x in speech_inputs]
									a_						:						List[str] 							= feat_extract.model_input_names[0]

									a_						:						Any 							= BatchFeature({input_name: speech_inputs}    )
									a_						:						List[Any] 							= min(_lowercase    )

									a_						:						Union[str, Any] 							= feat_extract.num_mel_bins  # hack!

									a_						:						Optional[Any] 							= feat_extract.pad(
									    _lowercase  ,      padding="""max_length"""  ,      max_length=_lowercase  ,      truncation=_lowercase  ,      return_tensors="""np"""    )
									self.assertIn("""attention_mask"""  ,      _lowercase    )
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape    )  ,      [processed_pad[input_name].shape[0], max_length]    )
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()  ,      [max_length for x in speech_inputs]    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									from datasets import load_dataset

									a_						:						str 							= load_dataset("""hf-internal-testing/librispeech_asr_dummy"""  ,      """clean"""  ,      split="""validation"""    )
									# automatic decoding with librispeech
									a_						:						List[str] 							= ds.sort("""id"""    ).select(range(_lowercase    )    )[:num_samples]["""audio"""]

									return [x["array"] for x in speech_samples]





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# fmt: off
									a_						:						Any 							= torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]    )
									# fmt: on

									a_						:						Union[str, Any] 							= self._load_datasamples(1    )
									a_						:						List[str] 							= SpeechTaFeatureExtractor()
									a_						:						Optional[int] 							= feature_extractor(_lowercase  ,      return_tensors="""pt"""    ).input_values
									self.assertEquals(input_values.shape  ,      (1, 93_680)    )
									self.assertTrue(torch.allclose(input_values[0, :30]  ,      _lowercase  ,      atol=1e-6    )    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									# fmt: off
									a_						:						str 							= torch.tensor(
									    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
									     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
									     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
									     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]    )
									# fmt: on

									a_						:						str 							= self._load_datasamples(1    )
									a_						:						Optional[int] 							= SpeechTaFeatureExtractor()
									a_						:						int 							= feature_extractor(audio_target=_lowercase  ,      return_tensors="""pt"""    ).input_values
									self.assertEquals(input_values.shape  ,      (1, 366, 80)    )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,      _lowercase  ,      atol=1e-4    )    )





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


